Reclaiming storage capacity in a dispersed storage network

ABSTRACT

A method for execution by an integrity processing unit includes determining to reclaim storage capacity of a DSN memory based on utilization information. Slice age information for a plurality of sets of encoded data slices stored in the DSN memory is determined, and a plurality of sets of encoded data slices are selected based on the slice age information. All encoded data slices are identified for deletion when substantially each of the selected plurality of sets includes less than or equal to a decode threshold number of encoded data slices. A subset of encoded data slices is identified for deletion when substantially each of the selected plurality of sets includes more than the decode threshold number of encoded data slices, where removal of the subset of encoded data slices results in at least the decode threshold number of encoded data slices remaining for each selected set of encoded data slices.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. §120 as a continuation-in-part of U.S. Utility applicationSer. No. 15/075,946, entitled “RE-ENCODING DATA IN A DISPERSED STORAGENETWORK”, filed Mar. 21, 2016, which claims priority pursuant to 35U.S.C. §119(e) to U.S. Provisional Application No. 62/168,114, entitled“RE-ENCODING DATA IN A DISPERSED STORAGE NETWORK”, filed May 29, 2015,both of which are hereby incorporated herein by reference in theirentirety and made part of the present U.S. Utility patent applicationfor all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention; and

FIG. 10 is a logic diagram of an example of a method of reclaimingstorage capacity in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

In various embodiments, each of the storage units operates as adistributed storage and task (DST) execution unit, and is operable tostore dispersed error encoded data and/or to execute, in a distributedmanner, one or more tasks on data. The tasks may be a simple function(e.g., a mathematical function, a logic function, an identify function,a find function, a search engine function, a replace function, etc.), acomplex function (e.g., compression, human and/or computer languagetranslation, text-to-voice conversion, voice-to-text conversion, etc.),multiple simple and/or complex functions, one or more algorithms, one ormore applications, etc. Hereafter, a storage unit may be interchangeablyreferred to as a dispersed storage and task (DST) execution unit and aset of storage units may be interchangeably referred to as a set of DSTexecution units.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each managing unit 18 and the integrity processing unit 20 maybe separate computing devices, may be a common computing device, and/ormay be integrated into one or more of the computing devices 12-16 and/orinto one or more of the storage units 36. In various embodiments,computing devices 12-16 can include user devices and/or can be utilizedby a requesting entity generating access requests, which can includerequests to read or write data to storage units in the DSN.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. Here, the computing device stores data object40, which can include a file (e.g., text, video, audio, etc.), or otherdata arrangement. The dispersed storage error encoding parametersinclude an encoding function (e.g., information dispersal algorithm(IDA), Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides dataobject 40 into a plurality of fixed sized data segments (e.g., 1 throughY of a fixed size in range of Kilo-bytes to Tera-bytes or more). Thenumber of data segments created is dependent of the size of the data andthe data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of another embodiment of dispersedstorage network (DSN) that includes a storage set 480, the network 24 ofFIG. 1, and the distributed storage and task integrity processing unit20 of FIG. 1. Alternatively, the integrity processing unit 20 may beimplemented utilizing one or more of a computing device 16 of FIG. 1and/or a storage execution unit 36 of FIG. 1. The storage set 480includes a set of DST execution (EX) units 1-5. Each DST execution unitincludes a memory 88, which can be implemented utilizing main memory 54of FIG. 2. Each DST execution unit may be implemented utilizing thestorage unit 36 of FIG. 1, utilized as distributed storage and task(DST) execution unit as described previously. Storage units canhereafter be interchangeably referred to as DST execution (EX) units.The DSN functions to reclaim storage capacity.

In various embodiments of the DSN, certain content is only useful for alimited period of time, or its value diminishes quickly with time, or isonly required to be stored for a certain period of time. For example,security camera footage, financial records, etc. may only need to bestored for a certain time period. In these cases, a requester canindicate a “time-value function”. The time-value function may be a stepfunction where it starts at some value and then drops to zero afterwards(this would implement an immediate future expiration for the content),an exponential decay function, or any other function which can bereasonably estimated at the time of write and/or computed at some futuretime. When writing data for an object with a known time value function,time-value function can be stored, either in the metadata associatedwith this object, or in the slices themselves. When storage resourcesneed to be reclaimed (e.g. when storage is over a quota, out ofcapacity, etc.) a “Value Optimization Agent” (VOA) can compare thetime-value function for existing objects or slices in a DSN memory,considering the time it was written, the current time, and thetime-value function. If the current value of the object is below acertain threshold, is the lowest among all sets of objects in the DSNmemory, and/or is lower than the storage cost (at the time ofconsideration) within the DSN memory, then the VOA can remove thisobject (or its slices) to recover additional storage capacity in thesystem. Further, properties of an IDA associated with the encoding ofthe slices can be used to balance value with reliability. For instance,different slices of the same source can be written with different timevalue functions, such that over time fewer slices of a given source willbe maintained, since as its value decreases, and the need to have itstored highly reliably decreases.

In an example of operation of the reclaiming of the storage capacity,the integrity processing unit 20 determines to reclaim the storagecapacity based on utilization information. The utilization informationcan include a storage capacity level of a memory 88, a storage capacitylevel of a DST execution unit, a storage utilization level of a memory88, and/or a storage utilization level of the DST execution unit. Thedetermining can include obtaining the utilization information (e.g.,receiving utilization information 1-5) and/or indicating to reclaim thestorage capacity when a level of storage capacity utilization is greaterthan a storage capacity utilization threshold level.

Having determined to reclaim the storage capacity, the integrityprocessing unit 20 can determine slice age information for a pluralityof sets of encoded data slices of each of a plurality of stored dataobjects, where a data object is dispersed storage error encoded toproduce a plurality of sets of encoded data slices that are stored inthe set of DST execution units 1-5. For example, a data object A isdivided into S data segments, where each data segment is dispersedstorage error encoded to produce a corresponding set of encoded dataslices 1-5 of S sets of encoded data slices 1-5. The determining caninclude interpreting a query response, interpreting an error message,and/or extracting the slice age information from metadata of theplurality of sets of encoded data slices. For example, the integrityprocessing unit 20 receives slice age information 1-5 from the DSTexecution units 1-5 with regards to the stored data object A including afirst set of encoded data slices A-1-1, A-2-1, A-3-1, A-4-1, and A-5-1through a Sth set of encoded data slices A-1-S, A-2-S, A-3-S, A-4-S, andA-5-S; and receives slice age information 1-5 from the DST executionunits 1-5 with regards to a stored data object B.

Having determined the slice age information, the integrity processingunit 20 selects a plurality of sets of encoded data slices of theplurality of storage data objects based on the slice age information.For example, the integrity processing unit 20 selects a plurality ofsets of encoded data slices associated with an oldest slice age of theplurality of stored data objects.

When substantially each set of the selected plurality of sets of encodeddata slices includes less than or equal to a decode threshold number ofencoded data slices, the integrity processing unit 20 can identify allencoded data slices of the selected plurality of sets of encoded dataslices of the selected plurality of sets of encoded data slices fordeletion. For example, the integrity processing unit 20 interprets theslice age information to determine a number of available encoded dataslices for each set.

When substantially each set of the selected plurality of sets of encodeddata slices includes more than the decode threshold number of encodeddata slices, for each set of encoded data slices of the selectedplurality of sets of encoded data slices, the integrity processing unit20 can identify at least one encoded data slice for deletion resultingin at least a decode threshold number of remaining available encodeddata slices of the set of encoded data slices. For example, theintegrity processing unit 20 selects a different storage unit for eachset of encoded data slices in a round-robin fashion to evenly distributecapacity reclamation, where the deleting repeats every n sets of encodeddata slices, where n is a parameter of an information dispersalalgorithm (IDA) associated with of the encoding of the data (e.g., n=5).For instance, when only one encoded data slice of each set of encodeddata slices is required for deletion, the integrity processing unit 20selects encoded data slices A-1-1, A-2-2, A-3-3, A-4-4, A-5-5, A-1-6,A-2-7, A-3-8, etc., for deletion. In another instance, when two encodeddata slices of each set of encoded data slices are required fordeletion, the integrity processing unit 20 additionally selects encodeddata slices A-2-1, A-3-2, A-4-3, A-5-4, A-1-5, A-2-6, A-3-7 etc., fordeletion where remaining encoded data slices of each set of encoded dataslices includes the decode threshold number of encoded data slices.

Having identified the encoded data slices for deletion, the integrityprocessing unit 20 facilitates removal of the identified encoded dataslices for deletion. For example, the integrity processing unit 20issues, via the network 24, delete slice requests 1-5 to the DSTexecution units 1-5 perform the deletion, where the delete slicerequests 1-5 includes identifiers of the identified encoded data slicesfor deletion.

In various embodiments, a processing system of an integrity processingunit includes at least one processor and a memory that storesoperational instructions, that when executed by the at least oneprocessor cause the processing system to determine to reclaim storagecapacity of a DSN memory based on utilization information. Slice ageinformation for a plurality of sets of encoded data slices stored in theDSN memory is determined, and a plurality of sets of encoded data slicesare selected based on the slice age information. All encoded data slicesof the selected plurality of sets of encoded data slices are identifiedfor deletion when substantially each set of the selected plurality ofsets of encoded data slices includes less than or equal to a decodethreshold number of encoded data slices. A subset of encoded data slicesof the selected plurality of sets of encoded data slices is identifiedfor deletion when substantially each set of the selected plurality ofsets of encoded data slices includes more than the decode thresholdnumber of encoded data slices, wherein removal of the subset of encodeddata slices results in at least the decode threshold number of encodeddata slices remaining for each set of encoded data slices of theselected plurality of sets of encoded data slices.

In various embodiments, the utilization information includes a storagecapacity level and/or a storage utilization level. In variousembodiments, determining to reclaim storage capacity of a DSN memory isbased on comparing a storage capacity utilization level to a storagecapacity utilization level. In various embodiments, the slice ageinformation is extracted from metadata of encoded data slices. Invarious embodiments, selecting the plurality of sets of encoded dataslices includes identifying and selecting an old plurality of sets ofencoded data slices associated with an oldest slice age of a pluralityof stored data objects.

In various embodiments, a number of available encoded data slices foreach of the plurality of sets of encoded data slices is determined, andthe number of available encoded data slices is compared to the decodethreshold number of data slices. In various embodiments, identifying thesubset of encoded data slices includes selecting encoded slices storedin different storage units for each set of encoded data slices byutilizing a round-robin selection strategy. In various embodiments, theround-robin selection strategy is based on an information dispersalalgorithm parameter. In various embodiments, the identified encoded dataslices are removed from storage. In various embodiments, a plurality ofdelete slice requests are generated for transmission to a plurality ofstorage units, wherein the plurality of delete slice requests includesidentifiers of the identified encoded data slices.

FIG. 10 is a flowchart illustrating an example of reclaiming storagecapacity of a DSN. In particular, a method is presented for use inassociation with one or more functions and features described inconjunction with FIGS. 1-9, for execution by an integrity processingunit that includes a processor or via another processing system of adispersed storage network that includes at least one processor andmemory that stores instruction that configure the processor orprocessors to perform the steps described below. Step 1002 includesdetermining to reclaim storage capacity of a DSN memory based onutilization information. For example, the processing module obtains theutilization information and indicates to reclaim the storage capacitywhen a level of storage capacity utilization is greater than a storagecapacity utilization threshold level.

The method continues at step 1004, which includes determining slice ageinformation for a plurality of sets of encoded data slices stored in theDSN memory. The determining includes at least one of interpreting aquery response, interpreting an error message, and extracting the sliceage information from metadata of encoded data slices.

The method continues at step 1006, which includes selecting a pluralityof sets of encoded data slices based on the slice age information. Forexample, the processing module identifies a plurality of sets of encodeddata slices associated with an oldest slice age of a plurality of storeddata objects (e.g., lowest priority for reliable ongoing storage). Themethod branches to step 1008 when an interpretation of the slice ageinformation indicates that each set of the selected plurality of sets ofencoded data slices includes more than a decode threshold number ofencoded data slices. The method continues to step 1008 when each set ofthe selected plurality of sets of encoded data slices includes less thanor equal to the decode threshold number of encoded data slices.

When substantially each set of the selected plurality of sets of encodeddata slices includes less than or equal to the decode threshold numberof encoded data slices, the method continues at step 1008 where theprocessing module identifies all encoded data slices of the selectedplurality of sets of encoded data slices for deletion. Whensubstantially each set of the selected plurality of sets of encoded dataslices includes more than the decode threshold number of encoded dataslices, for each set of encoded data slices of the selected plurality ofsets of encoded data slices, the method continues at step 1010 where theprocessing module identifies at least one encoded data slice fordeletion resulting in at least the decode threshold number of encodeddata slices remaining. Having identified the encoded data slices fordeletion, the processing module facilitates deletion of the identifiedencoded data slices for deletion.

In various embodiments, a non-transitory computer readable storagemedium includes at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to determine to reclaim storage capacity of a DSNmemory based on utilization information. Slice age information for aplurality of sets of encoded data slices stored in the DSN memory isdetermined, and a plurality of sets of encoded data slices are selectedbased on the slice age information. All encoded data slices of theselected plurality of sets of encoded data slices are identified fordeletion when substantially each set of the selected plurality of setsof encoded data slices includes less than or equal to a decode thresholdnumber of encoded data slices. A subset of encoded data slices of theselected plurality of sets of encoded data slices is identified fordeletion when substantially each set of the selected plurality of setsof encoded data slices includes more than the decode threshold number ofencoded data slices, wherein removal of the subset of encoded dataslices results in at least the decode threshold number of encoded dataslices remaining for each set of encoded data slices of the selectedplurality of sets of encoded data slices.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by an integrity processingunit that includes a processor, the method comprises: determining toreclaim storage capacity of a DSN memory based on utilizationinformation; determining slice age information for a plurality of setsof encoded data slices stored in the DSN memory; selecting a pluralityof sets of encoded data slices based on the slice age information;identifying all encoded data slices of the selected plurality of sets ofencoded data slices for deletion when substantially each set of theselected plurality of sets of encoded data slices includes less than orequal to a decode threshold number of encoded data slices; andidentifying a subset of encoded data slices of the selected plurality ofsets of encoded data slices for deletion when substantially each set ofthe selected plurality of sets of encoded data slices includes more thanthe decode threshold number of encoded data slices, wherein removal ofthe subset of encoded data slices results in at least the decodethreshold number of encoded data slices remaining for each set ofencoded data slices of the selected plurality of sets of encoded dataslices.
 2. The method of claim 1, wherein the utilization informationincludes at least one of: a storage capacity level or a storageutilization level.
 3. The method of claim 1, wherein determining toreclaim storage capacity of a DSN memory is based on comparing a storagecapacity utilization level to a storage capacity utilization level. 4.The method of claim 1, wherein the slice age information is extractedfrom metadata of encoded data slices.
 5. The method of claim 1, whereinselecting the plurality of sets of encoded data slices includesidentifying and selecting an old plurality of sets of encoded dataslices associated with an oldest slice age of a plurality of stored dataobjects.
 6. The method of claim 1, further comprising determining anumber of available encoded data slices for each of the plurality ofsets of encoded data slices; and comparing the number of availableencoded data slices to the decode threshold number of encoded dataslices.
 7. The method of claim 1, wherein identifying the subset ofencoded data slices includes selecting encoded slices stored indifferent storage units for each set of encoded data slices by utilizinga round-robin selection strategy.
 8. The method of claim 7, wherein theround-robin selection strategy is based on an information dispersalalgorithm parameter.
 9. The method of claim 1, further comprisingremoving the identified encoded data slices from storage.
 10. The methodof claim 1, further comprising generating a plurality of delete slicerequests for transmission to a plurality of storage units, wherein theplurality of delete slice requests includes identifiers of theidentified encoded data slices.
 11. A processing system of an integrityprocessing unit comprises: at least one processor; a memory that storesoperational instructions, that when executed by the at least oneprocessor cause the processing system to: determine to reclaim storagecapacity of a DSN memory based on utilization information; determineslice age information for a plurality of sets of encoded data slicesstored in the DSN memory; select a plurality of sets of encoded dataslices based on the slice age information; identify all encoded dataslices of the selected plurality of sets of encoded data slices fordeletion when substantially each set of the selected plurality of setsof encoded data slices includes less than or equal to a decode thresholdnumber of encoded data slices; and identify a subset of encoded dataslices of the selected plurality of sets of encoded data slices fordeletion when substantially each set of the selected plurality of setsof encoded data slices includes more than the decode threshold number ofencoded data slices, wherein removal of the subset of encoded dataslices results in at least the decode threshold number of encoded dataslices remaining for each set of encoded data slices of the selectedplurality of sets of encoded data slices.
 12. The processing system ofclaim 11, wherein the utilization information includes at least one of:a storage capacity level or a storage utilization level.
 13. Theprocessing system of claim 11, wherein determining to reclaim storagecapacity of a DSN memory is based on comparing a storage capacityutilization level to a storage capacity utilization level.
 14. Theprocessing system of claim 11, wherein the slice age information isextracted from metadata of encoded data slices.
 15. The processingsystem of claim 11, wherein selecting the plurality of sets of encodeddata slices includes identifying and selecting an old plurality of setsof encoded data slices associated with an oldest slice age of aplurality of stored data objects.
 16. The processing system of claim 11,wherein the operational instructions, when executed by the at least oneprocessor, further cause the processing system to: determine a number ofavailable encoded data slices for each of the plurality of sets ofencoded data slices; and compare the number of available encoded dataslices to the decode threshold number of encoded data slices.
 17. Theprocessing system of claim 11, wherein identifying the subset of encodeddata slices includes selecting encoded slices stored in differentstorage units for each set of encoded data slices by utilizing around-robin selection strategy.
 18. The processing system of claim 17,wherein the round-robin selection strategy is based on an informationdispersal algorithm parameter.
 19. The processing system of claim 11,wherein the operational instructions, when executed by the at least oneprocessor, further cause the processing system to generate a pluralityof delete slice requests for transmission to a plurality of storageunits, wherein the plurality of delete slice requests includesidentifiers of the identified encoded data slices.
 20. A non-transitorycomputer readable storage medium comprises: at least one memory sectionthat stores operational instructions that, when executed by a processingsystem of a dispersed storage network (DSN) that includes a processorand a memory, causes the processing system to: determine to reclaimstorage capacity of a DSN memory based on utilization information;determine slice age information for a plurality of sets of encoded dataslices stored in the DSN memory; select a plurality of sets of encodeddata slices based on the slice age information; identify all encodeddata slices of the selected plurality of sets of encoded data slices fordeletion when substantially each set of the selected plurality of setsof encoded data slices includes less than or equal to a decode thresholdnumber of encoded data slices; and identify a subset of encoded dataslices of the selected plurality of sets of encoded data slices fordeletion when substantially each set of the selected plurality of setsof encoded data slices includes more than the decode threshold number ofencoded data slices, wherein removal of the subset of encoded dataslices results in at least the decode threshold number of encoded dataslices remaining for each set of encoded data slices of the selectedplurality of sets of encoded data slices.